1. Technical Field
The present disclosure relates to machine condition monitoring and, more specifically, to Bayesian sensor estimation for machine condition monitoring.
2. Discussion of the Related Art
Condition monitoring relates to the observation and analysis of one or more sensors that sense key parameters of machinery. By closely observing the sensor data, a potential failure or inefficiency may be detected and remedial action may be taken, often before a major system failure occurs.
Effective condition monitoring may allow for increased uptime, reduced costs associated with failures, and a decreased need for prophylactic replacement of machine components.
Condition monitoring may be applied to a wide variety of industrial machinery such as capitol equipment, factories and power plants; however, condition monitoring may also be applied to other mechanical equipment such as automobiles and non-mechanical equipment such as computers. In fact, principals of condition monitoring may be applied more generally to any system or organization. For example, principals of condition monitoring may be used to monitor the vital signs of a patient to detect potential health problems. For example, principals of condition monitoring may be applied to monitor performance and/or economic indicators to detect potential problems with a corporation or an economy.
In condition monitoring, one or more sensors may be used. Examples of commonly used sensors include vibration sensors for analyzing a level of vibration and/or the frequency spectrum of vibration. Other examples of sensors include temperature sensors, pressure sensors, spectrographic oil analysis, ultrasound, and image recognition devices.
A sensor may be a physical sensory device that may be mounted on or near a monitored machine component or a sensor may more generally refer to a source of data.
Conventional techniques for condition monitoring acquire data from the one or more sensors and analyze the collected data to detect when the data is indicative of a potential fault. Inferential sensing is an example of an approach that may be used to determine when sensor data is indicative of a potential fault.
In inferential sensing, an expected value for a particular sensor {circumflex over (x)} is estimated, for example, through the use of other observed sensors y. The actual sensor value y may then be compared to the expected sensor value {circumflex over (x)}, and the larger the difference between the two values, the greater the likelihood of a potential fault.
Accordingly, fault diagnosis is typically performed in two steps. In the first step, based on observed sensor values y, the expected particular sensor value {circumflex over (x)} is calculated. This step is known as “sensor estimation.” The “residue” is defined as the difference between the estimated value {circumflex over (x)} and the observed value y. Then, in a “rule-based decision step,” the values of y, {circumflex over (x)} and the residue are analyzed with respect to a set of rules to properly identify the presence of and type of a potential fault.
The set of rules are generally defined by experts familiar with the correct operations of the system being monitored.
In the sensor estimation step, a variety of techniques may be used to provide the estimated value {circumflex over (x)} based on the set of observed sensor values y. Such approach may involve the use of multivariate state estimation techniques (MSET) or auto-associate neural networks (AANN). Details concerning these approaches may be found, for example, in J. W. Hines, A. Gribok and B. Rasmussen (2001), “On-Line Sensor Calibration Verification: A survey,” International Congress and Exhibition on Condition Monitoring and Diagnostic Engineering Management, incorporated herein by reference.
In each of these methods, involve the building of multiple-input-multiple-output networks to calculate {circumflex over (x)} based on the set of observed sensor values y. The networks themselves may be established base on a set of training data that includes the sensor values X and Y observed during fault-free operation of the system being monitored. Later, when the system being monitored is brought on-line, observed faults may be difficult to detect because the characteristics of the observed faults would not have been observed during the period of collection of the training data.
Accordingly, artificial faults may be added to the training data as is done in D. Wrest, J. W. Hines, and R. E. Uhrig (1996), “Instrument Surveillance and Calibration Verification Through Plant Wide Monitoring Using Autoassociative Neural Networks,” The American Nuclear Society International Topical Meeting on Nuclear Plant Instrumentation, Control and Human Machine Interface Technologies, May 6-9, 1996, incorporated herein by reference. However, it may be difficult or impossible to obtain artificial fault data for every conceivable fault, given the number of possible deviations and the high dimensionality of the sensor vector.
Another approach to providing the estimated value {circumflex over (x)} based on the set of observed sensor values y involves support vector regression (SVR). Examples of such approaches may be found in A. V. Gribok, J. W. Hines and R. E. Uhrig (2000), “Use of Kernel Based Techniques for Sensor Validation in Nuclear Power Plants,” International Topical Meeting on Nuclear Plant Instrumentation, Controls and Human-Machine Interface Technologies, incorporated herein by reference.
In SVR, an estimate for a particulate sensor Xi ({circumflex over (x)}i) is determined from the observed values of other sensors yj≠i. SVR makes the basic assumption that Xi is predictable from yj≠i, however, this assumption often does not hold true. For example, in a power plant, a process driver inlet temperature is not accurately predictable based on any other sensor values. Additionally, a fault within the sensors yj≠i may result in an inaccurate estimation for Xi, even if the i-th sensor is normal.